法人名の表記を正規化

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Since no annotations are provided, the description carries the full burden. It discloses behavioral traits (conversions, old character handling) and a limitation (dictionary incomplete), but does not specify the return value structure or error behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences: the first lists the core actions and scope, the second states the limitation. No extraneous detail, with the main function front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity and lack of output schema, the description should clarify what the returned result contains (e.g., whether both normalized full name and body are returned). The limitation is disclosed, but the return format remains ambiguous.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema describes the single parameter as 'corporate name to normalize,' while the description enriches this by listing the types of variations handled (e.g., full-width/half-width, legal entity conversions), giving the agent a clearer idea of acceptable inputs.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it normalizes Japanese corporate name notation variations (e.g., 株式会社 vs (株)) and separates the legal entity to return the main name. This distinguishes it from sibling tools like validate_corporate_number or parse_corporate_number, which focus on validation or parsing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for Japanese company names with notation variations, but does not explicitly state when to use this tool versus alternatives. The limitation that the dictionary covers only major forms suggests it may not be suitable for exhaustive coverage, serving as a soft exclusion.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.